Branch of Machine Learning dealing with Supervised Learning.
Instance-based learning is a machine learning technique that relies on storing and recalling instances or examples of training data. You may have also heard of instance-based learning, referenced as memory learning or lazy learning. This is for a good reason,…
One common task you will encounter while working in machine learning is clunky datasets that need cleaning. This involves dealing with inconsistencies and errors in the data and data that wouldn’t “make sense” for our models in their current form.…
If you’re new to machine learning or want a quick refresher on what get_dummies does, you’ve found the right place. This blog post will quickly give an overview of what get_dummies is and how it can be used correctly in…
In machine learning, there are two types: those who like to keep things short and sweet and those who want to explain everything in detail. I fall into the latter category – I love verbosity. Some might call it overkill,…
Heatmaps are great for quickly visualizing data that normally isn’t easy to ingest. However, it sometimes feels impossible to find a coding resource that shows you how to code up these heatmaps in Python, and what they’ll look like when…
The Gini Index and Entropy are two important concepts in decision trees and data science. While both seem similar, underlying mathematical differences separate the two. Understanding these subtle differences is important as one may work better for your machine learning…
While you may be more familiar with choosing Precision and Recall for your machine learning algorithms, there is a statistic that takes advantage of both. The F1 Score is a statistic also used to measure the performance of a machine-learning…
Sometimes mistakenly used interchangeably, we’re here to tell you that a parameter is not a variable. However, depending on the context – a parameter and a variable can mean many different things. In this post, we’ll break down the following:…
In data science, there are many different ways to slice the pie. While many refer to independent and dependent variables differently, they usually mean the same thing. Your predictor variables are your independent variables, and with these, you’ll (hopefully) be…
In some machine learning problems, it’s not uncommon to have thousands of variables. While this is nice, keeping that many variables in your dataset will lead to many problems down the road. But don’t worry, we’ll teach you how to…